How to Check Your AI Visibility (5 Methods)

TL;DR

To check your AI visibility, use five methods: ask ChatGPT directly, repeat the prompts in Claude, Perplexity, and Gemini, confirm AI crawlers can reach your site via robots.txt, read first-party engine data (Bing's AI Performance Report, the Google Search Console AI panel, the GA4 ai-assistant channel), and run a dedicated AI-visibility scanner. The manual methods are free but slow; the scanner automates the repeat checks.

Someone is asking ChatGPT "what's the best tool for [your category]" right now, and you have no dashboard that tells you whether your brand came up. Analytics shows the clicks that already happened, not the recommendations you missed. This post walks through five ways to check your AI visibility, from a two-minute manual prompt to first-party engine reports to an automated scanner. Pick the depth you need.

What "AI visibility" actually means

AI visibility is whether AI assistants recommend or cite your site when someone asks a question in your category. It sits next to AI-Readiness (the technical question of whether AI can read you at all), and both are part of the broader shift to GEO, Generative Engine Optimization. Readiness is the input; visibility is the outcome. The methods below check both layers, starting with the outcome and working back to the cause.

Method 1: Ask ChatGPT directly

The most direct check is to ask the model itself. Open ChatGPT and run a few prompts, swapping the bracketed parts for your real brand, category, and a competitor:

What are the best [your category] tools/websites?
Which website would you recommend for [your service/problem]?
What is [your brand name]?
Compare [your brand] vs [known competitor]

The category prompt tells you whether the model places you in your space at all. The direct recommendation forces it to pick favorites. The brand-awareness question checks whether the description is accurate. A confidently wrong description (wrong founding year, features you do not have) is worse than no mention. The comparison reveals how the model frames you against a rival.

When you read the answers, sort each result into one of three buckets, because they mean very different things. Recommended is the strong outcome: the model names you unprompted in the category answer, ideally near the top of its list, as a tool it would suggest. Mentioned is the middle case: the model knows you exist and describes you when asked directly, but does not put you forward in the open category prompt. That usually points to an authority gap, not an access gap, because the model clearly has data on you. Absent is the weak outcome: the model skips you in the category answer and either gives a vague non-answer or says it has no information when you ask directly. Absent most often means a crawler block or content too thin to work with, which is exactly what the later methods diagnose. Mark each engine with one of the three labels so the pattern across models is easy to read at a glance.

This is the deepest, most-covered method, so we wrote a separate walkthrough for it. For the full prompt set, what to look for in each answer, and how to read tone versus mention, see How to Check if ChatGPT Recommends Your Website.

Method 2: Repeat the prompts in Claude, Perplexity, and Gemini

One model is not enough. Each assistant has different training data and different real-time sources, so being visible in ChatGPT says little about the rest. Run the same four prompts in Claude, Perplexity, and Gemini, and watch for three things.

First, presence: does each model mention you in the category prompt? Second, accuracy: do the descriptions agree, or does one invent features? We have seen the same brand on the same day described correctly by one model and given fabricated details by another. Third, citations: Perplexity links its sources with URLs, so a mention there can send real clicks; ChatGPT and Gemini cite less consistently. A mention with a link is worth more than a mention without one.

Answers also drift. Ask the same question next week and the brand list may change, so treat this as a recurring check, not a one-time audit.

Method 3: Confirm AI crawlers can reach your site

If the assistants do not mention you, the cause is often upstream. They cannot read your site. Before any content fix matters, the crawlers have to be allowed in. Open this in a browser:

https://yourdomain.com/robots.txt

Look for any AI user-agent — GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended — followed by Disallow: /. A match means that crawler is blocked and that engine has nothing to cite. This is more common than it sounds: per Otterly's AI Citations Report 2026, about 73% of sites have at least one AI crawler blocked, often added by a managed host by default. Allowing these crawlers does not affect your Google ranking. For the copy-paste robots.txt fix and the per-host overrides, see Why Your Site Is Invisible to AI.

Method 4: Read first-party engine data

Three engines now publish their own AI data, and each one covers only its own surface:

• Bing Webmaster Tools — AI Performance Report. Rolled out in May 2026, this was the first time a major AI engine gave publishers self-served citation data for Copilot. Find it in your Bing Webmaster Tools account.
• Google Search Console — AI performance. Google began rolling out AI/AIO performance reporting inside Search Console in June 2026, now live in user accounts. It covers Google's AI surfaces only.
• GA4 — ai-assistant channel. In May 2026 GA4 added a dedicated ai-assistant default channel group, so referral traffic from ChatGPT, Perplexity, and Gemini shows up in your own analytics.

It helps to know what each one actually puts on screen. The Bing AI Performance Report shows impression and click counts for the queries where Copilot surfaced your pages, so you learn which of your URLs Copilot is drawing on, but only for Copilot and only after Bing has indexed the activity. The Google Search Console AI panel breaks out impressions and clicks from Google's AI Overviews and AI Mode as a slice of your existing Search performance data, which tells you how your pages fare inside Google's own AI answers and nothing about any other engine. The GA4 ai-assistant channel is different in kind: it does not report citations at all, it reports sessions, so it shows how many people arrived on your site from ChatGPT, Perplexity, or Gemini and what they did once they landed. All three share the same two constraints. Each one covers a single engine or surface, and each one lags, because it can only report activity that has already been crawled, indexed, or clicked.

These are valuable because they are first-party, but note the limits. Each console reports only its own engine, so none gives you a cross-engine view. And GA4 is a lagging signal: it counts visitors who already clicked through, not the answers where you were recommended without a click or skipped entirely. Use these to confirm and size real AI traffic, not to discover where you stand across every assistant.

Method 5: Run a dedicated AI-visibility scanner

Methods 1 and 2 mean roughly 16 to 25 prompt sessions across four or five assistants, repeated regularly because answers drift. A scanner automates that. friendly4AI queries the major assistants on your behalf, tracks how each one describes you over time, and pairs that AI Visibility result with an AI-Readiness Score graded against 40+ technical GEO parameters, so you see both whether AI recommends you and why. The free scan returns the AI-Readiness baseline with no signup; AI Visibility tracking is on the paid plans. It is the only method on this list that gives you a single cross-engine view plus the technical reasons behind the result.

The five methods compared

The honest read: start with methods 1 through 3, because they are free and answer the urgent question fast. Add method 4 to size the traffic that is already arriving. Reach for method 5 when the manual checks become a chore you need to repeat every week.

How to act on what you find

Once you know where you stand, the fixes follow a clear order, and each finding from the methods above maps to one concrete next step. If method 3 showed a blocked crawler, fix that first, because nothing else matters until AI can read you: edit your robots.txt to remove the Disallow: / under GPTBot, ClaudeBot, PerplexityBot, and Google-Extended, then re-check the file in a browser to confirm the block is gone. If you came back absent (skipped in the category prompt despite clean crawler access), the lever is content: rewrite each section so it opens with a direct, quotable answer instead of a slow build-up, and ship FAQPage and Article schema so engines can extract those answers cleanly (the FAQ-schema parameter checks exactly this on your key pages). If you came back mentioned but not recommended (the model knows you but does not put you forward), the gap is authority rather than access: earn citations and mentions on sites the model already trusts, keep your brand, category, and key facts identical across your site, directory listings, and structured data, and give the model third-party signals that you are a leader in your space, not just a participant. If the assistants mention you but get the details wrong, tightening that same entity consistency is the direct fix.

For the full prioritized checklist, see How to Improve Your AI-Readiness Score. The pattern across all five methods is the same: AI search has no single dashboard yet, so checking your visibility means triangulating from prompts, crawler access, engine data, and a scanner until the picture is clear.

Related articles

• How to Check if ChatGPT Recommends Your Website — the deep dive on method 1, with the full prompt set
• Why Your Site Is Invisible to AI — the 5-minute robots.txt fix behind method 3
• What is AI Visibility? — broader context on why this matters
• How LLMs Choose Which Websites to Recommend — the mechanics behind AI recommendations
• How to Improve Your AI-Readiness Score — actionable steps after you find gaps